A Control Theory Approach to Improve Microprocessors for Real-Time Applications by Self-Adapting Thread Performance

2012 ◽  
pp. 1-24
Author(s):  
Uwe Brinkschulte ◽  
Daniel Lohn ◽  
Mathias Pacher ◽  
Michael Bauer
Keyword(s):  
Control Theory ◽  
Real Time ◽  
Theory Approach ◽  
Real Time Applications

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications

Wave Forecasts in Muddy Coastal Environments: Model Development and Real-Time Applications

2004 ◽  
Author(s):  
Alexandru Sheremet ◽  
Gregory W. Stone ◽  
James M. Kaihatu
Keyword(s):  
Real Time ◽  
Model Development ◽  
Coastal Environments ◽  
Real Time Applications

Development of a Multi-Rate Simulation Model of an Unmanned Underwater Vehicle for Real-Time Applications

2009 ◽  
Vol 18 (3-4) ◽  
pp. 47-54 ◽  
Author(s):  
J.J. Zenor ◽  
D.J. Murray-Smith ◽  
E.W. McGookin ◽  
R.E. Crosbie
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Underwater Vehicle ◽  
Unmanned Underwater Vehicle ◽  
Real Time Applications

Peak-Power-Aware Energy Management for Periodic Real-Time Applications

2020 ◽  
Vol 39 (4) ◽  
pp. 779-788 ◽  
Author(s):  
Mohsen Ansari ◽  
Amir Yeganeh-Khaksar ◽  
Sepideh Safari ◽  
Alireza Ejlali
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Peak Power ◽  
Real Time Applications

Effects of multilevel security on real-time applications

2002 ◽  
Author(s):  
R.K. Clark ◽  
I.B. Greenberg ◽  
P.K. Boucher ◽  
T.F. Lunt ◽  
P.G. Neumann ◽  
...  
Keyword(s):  
Real Time ◽  
Multilevel Security ◽  
Real Time Applications

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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